045. EVALUATION OF NOVEL SERUM BIOMARKERS OF DISEASE ACTIVITY IN GIANT CELL ARTERITIS, TAKAYASU’S ARTERITIS, POLYARTERITIS NODOSA, AND EOSINOPHILIC GRANULOMATOSIS WITH POLYANGIITIS
Notice bibliographique
Résumé
Background: Better biomarkers are needed for clinical assessment of vasculitis. This study assessed potential circulating biomarkers of disease activity in giant cell arteritis (GCA), Takayasu’s arteritis (TAK), polyarteritis nodosa (PAN) and eosinophilic granulomatosis with polyangiitis (EGPA, Churg-Strauss). Methods: A panel of 22 serum proteins was tested in patients enrolled in longitudinal cohorts of patients with GCA, TAK, PAN, or EGPA. Biomarker data were ln-transformed when appropriate to reduce skewing. Mixed models were used for most analyses, with biomarker level as the dependent variable. Correlation coefficients were also calculated. A J48 classification tree method was used to find the most relevant markers to differentiate between active and inactive GCA. Results: 418 samples from 152 patients (60 GCA, 29 TAK, 26 PAN, 37 EGPA) were tested. Most patients were on treatment. In GCA, BCA-1/CXCL13, ESR, IP-10/CXCL10, sIL-2R, and TIMP-1 showed significant (P < 0.05) differences during active disease with or without adjustment for treatment. In EGPA, BCA-1/CXCL13, G-CSF, GM-CSF, IL-6, IL-15, and sIL-2R were higher in active disease (Table 1). In PAN, ESR and MMP-3 were higher in active disease, and no significant markers were identified in TAK. The correlations of ESR or CRP with the experimental markers were all ≤ r = 0.25. Differences in marker levels between diseases were significant in mixed models for 11 markers, after adjustment for disease activity or treatment, and were more striking (all P < 0.01) than differences related to disease activity or treatment: BCA-1/CXCL13, CRP, ESR, G-CSF, GM-CSF, IL-6, IL-8, IL-18BP, IP-10/CXCL10, MMP-3, and sIL-2R. Using a classification tree, a combination of TIMP-1, IL-6, INF-ɣ, and MMP-3 correctly classified 87% of patients with inactive GCA, but the method did not identify a combination of markers that correctly classified more than 50% of patients with active GCA. Biomarkers associated with active GCA, PAN, or EGPA Mixed effects models included marker concentration as the dependent variable, disease activity as a dichotomous independent variable, the patient as the random effect, with (“Meds”) or without current use of prednisone and other immunosuppressive drugs as two additional dichotomous variables. Numbers indicate beta-coefficients associating an increase (if > 0) or decrease (if < 0) in marker concentration with active disease, with 95% confidence intervals in parentheses, and P-values. All marker values except ESR were ln-transformed. Therefore, the beta-coefficient for ESR represents absolute change (mm/hr), whereas for other markers, the beta-coefficient multiplied by 2.72 represents fold-change. Only analyses with P < 0.05 are shown. CRP was also one of the markers in this study, with no P < 0.05. Conclusion: This study identified several biomarkers of disease activity in GCA and EGPA, with few significant markers shared between diseases. Levels of 11 markers differed between diseases, after adjusting for disease activity and treatment. Further studies are needed to confirm these findings and better understand potential clinical uses of these markers. Disclosures: Dr. Merkel reports receiving funds for the following activities: Consulting: AbbVie, AstraZeneca, Biogen, Boeringher-Ingelheim, Bristol-Myers Squibb, Celgene, ChemoCentryx, Genentech/Roche, Genzyme/Sanofi, GlaxoSmithKline, InflaRx, Insmed, Jannsen, Kiniksa; Research Support: AstraZeneca, Boeringher-Ingelheim, Bristol-Myers Squibb, Celgene, ChemoCentryx, Genentech/Roche, GlaxoSmithKline, Kypha, TerumoBCT; Royalties: UpToDate. This work was sponsored by the Vasculitis Clinical Research Consortium and received support from the National Institutes of Health: U54 AR057319, RC1 AR 058303, P60 AR047785, and N01 AI15416.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».